Forecasting Demand
Step 1: Lacking any additional information or data, one of the best predictors of what will happen in the future is what has happened in the past. Based on the average performance
Essential RM Term
Trailing period: A data collection method characterized by the act of discarding the oldest piece of data in a data set when the newest data are added, thus updating the set’s information while keeping the set size constant. Data contained in a trailing period are often used in calculating a rolling average.
*Some RMs choose to assess GOPPAR as an alternative to RevPAR (see Chapter 9). The rationale for doing so is compelling. Currently, however, RevPAR is the most commonly utilized revenue statistic reported in the lodging industry and thus it will be used for the majority of the revenue management decision making illustrations presented in the text.
Figure 6.3 Your Hotel Operating Data (Trailing 8 week average)
Average
ADR $158.75 $188.75 $148.75 $138.75 $155.75 $159.75 $129.75
RevPAR $139.70 $171.76 $116.03 $ 92.96 $ 70.09 $ 81.47 $ 37.63 c06RevenueManagementforHoteliers168 Page 168 9/6/10 9:43:30 PM user-f391
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of your hotel over the past eight weeks, your estimate for the RevPAR3 you would likely achieve on the Monday three weeks from now is:
$158.75 (trailing ADR) ⫻ 0.88 (trailing Occupancy %) ⫽ $139.70 RevPAR forecast Step 2: If 100 rooms are sold at $109.00, it is reasonable to assume a sellout of all remaining available rooms. The estimated RevPAR generated in such a case would be calculated as:
$109.00 (proposed tour operator rate) ⫻ 0.50 (Occupancy % @ 100 rooms) ⫽ $ 54.50 Plus
$158.75 (trailing ADR) ⫻ 0.50 (Occupancy % with sellout) ⫽ $ 79.38
Total RevPAR $ 133.88
Step 3: The difference between the two RevPAR estimates is $5.82 ($139.70 ⫺ $133.88 ⫽
$5.82). Thus, your hotel would actually achieve a lower RevPAR by accepting the tour operator’s proposal and selling out the hotel than it would if the proposal was rejected.
Experienced RMs, however, recognize that additional factors must be considered before a proper recommendation to accept or reject the tour operator’s proposal could be made. Among these additional factors are:
Revenue impact
Expense impact
Impact on future pricing
Let’s look at the importance of these three areas when applied to the current example:
Revenue impact. This refers to the impact on total hotel revenue of the additional rooms sales. If the hotel offers additional services such as food, beverages, in-room services, spa services, or other opportunities for guest purchases, the additional revenues that could be generated from a sellout (200 rooms sold in this example) compared to the 88 percent occupancy (176 rooms sold) might affect the decision you would make. In this specifi c example, it is unlikely that the additional revenue would cause you to want to accept the group. Knowing exactly how to make this critical calculation is important, however, and in Chapter 13 of this book you will learn how to do it.
Expense impact. This refers to the additional costs that would be incurred through the servicing of the additional 24 sold rooms (200 rooms ⫺ 176 rooms ⫽ 24 additional rooms). Room cleaning costs, in-room supplies, and amenities used and additional labor costs required in other areas of the hotel may be additional expense factors to consider.
Impact on future pricing. This refers to the impact on the remaining rooms’
selling prices if in fact 100 rooms are “presold” on that specifi c Monday.
Note that the ADR estimated for your property is $158.75 (see Figure 6.3)
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CH A P T E R 6 F O R E C A ST I N G D E M A N D
at a forecasted occupancy of 88.5 percent. If, however, a sellout is forecast, discounts that may have been offered to guests unwilling to pay full rack rate for your remaining rooms should likely be scaled back or eliminated. Taking the tour group could result in the elimination of discounted rates and thus an increased ADR for all unsold, non–tour-committed rooms. As a result, a higher RevPAR may be achieved for those nongroup rooms and, as a result, the entire property.
This example illustrates that total revenues, operating expenses, and impact on future selling prices should be assessed by RMs when they utilize historical as well as other types of data in their revenue management decision making.
The specifi c historical data that may be of interest to RMs and thus should be regularly collected for future analysis will vary somewhat by property but would typically include the data related to the following:
Number of reservations/ room nights booked per day
Number of reservations/ room nights denied per day
Number of daily reservation cancellations
Total number of room nights canceled
Number of check-ins (arrivals)
Number of check-outs (departures)
No-shows
Walk-ins
ADR achieved
Occupancy % achieved
By the property
By room type
Average number of guests per room
Average length of guest stay
In many cases, RMs who have collected historical data will want to summarize it. Thus, for example, an RM may seek to monitor, or track, historic ADR levels or any other relevant measurement on a daily, weekly and/or monthly basis.
In many cases, RMs track their data by calculating mathematical averages. Inexpe-rienced RMs seeking to summarize data often make a mistake when calculating their averages. To better understand why they sometimes have diffi culty and to avoid such mistakes yourself, consider the hotel that achieves a $300.00 ADR on Monday and a
$320.00 ADR on Tuesday.
In this scenario, the RM seeks to summarize the Monday and Tuesday ADR data by calculating an average ADR for the two days. Such an RM would not add the Monday ADR to the Tuesday ADR and arrive at a $310.00 two-day average ADR.
Essential RM Terms
Denied (reservation): The situation that occurs when a hotel is unable to accommodate a guest’s reservation preference due to the unavailability of the room or service at the price, or on the date requested by the guest. Also known as a denial.
Walk-in: A guest that arrives at the property seeking a room but without an advanced reservation.
Essential RM Term
Track (data): To continually monitor a set of data.
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$310.00 is not likely to be the two-day ADR average for that hotel. To understand why, recall that the formula for ADR is:
Total room revenue
Total rooms sold 5 ADR
Figure 6.4 illustrates the proper method used to calculate the hotel’s actual average or mean ADR.
Recall from Chapter 3 that an arithmetic mean is the mathematical average of a set of numbers. A mean is calculated by adding up two or more values (scores) and dividing the total by the number of values (scores). Note that the proper denominator in the calculation of mean ADR in this example is 500 (the number of rooms sold), not 2 (the number of days’
ADR included in the average)! Properly calculating the mean produces an average ADR of
$312.00, not $310.00.
For RMs, two additional types of means, or averages, are important to understand.
These are the fi xed average and the rolling average.
A fi xed average is an average in which an RM would determine a specifi c time period—for example, the fi rst 14 days of a given month—and then compute the average amount of revenue generated, rooms sold or other relevant data based on results for that defi ned period.
Note that this average is called fi xed because the fi rst 14 days of the month will always consist of the same days (days 1–14 of each month). To illustrate, consider the RM of the Lafayette-Lincoln Lodge, a small hotel property that wishes to calculate a fi xed average of revenue generation for the fi rst 14 days of the month. Figure 6.5 details the revenue generated for this period and the average (mean) revenue generated per day.
This average revenue is calculated as: Total revenue ⫼ Number of days. In Figure 6.5,
$19,980Ⲑ14 ⫽ $1,427.14 per day. It is a fi xed or constant average because the RM at the Lafay-ette-Lincoln Lodge has identifi ed the 14 specifi c days that are used to make up the average.
The number $1,427.14 may be very useful because it might, if the RM decides it makes sense to do so, be used as a good predictor of the revenue volume that should be expected for the fi rst 14 days of next month.
The rolling average is the average amount of sales revenue, rooms sold, or other data over a changing time period. Essentially, while a fi xed average is computed using a specifi c and con-stant set of data, the rolling average is computed using trailing data that will change regularly.
Essential RM Term
Fixed average: An average calculated by using historical data generated during a specifi c and unchanged time period.
Rolling average: An average calculated by using historical data generated during a changing time period.
H I STO R I C A L DATA 171
Date Revenue Sold Rooms Sold ADR
Monday $ 60,000 200 $300.00
Tuesday $ 96,000 300 $320.00
Mean ADR $156,000 500 $312.00
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Figure 6.5 14-Day Fixed-Average Revenue Calculation Lafayette-Lincoln Lodge
Day Daily Revenue
1 $ 1,350.00
2 1,320.00
3 1,390.00
4 1,440.00
5 1,420.00
6 1,458.00
7 1,450.00
8 1,460.00
9 1,410.00
10 1,440.00
11 1,470.00
12 1,460.00
13 1,418.00
14 1,494.00
14-day Total $19,980.00
To illustrate a rolling average, consider the case of the RM who manages the Douglas Lodge, also a small hotel property. She is interested in knowing the average revenue dollars generated by her property for each prior seven-day period. Obviously, in this case, the prior seven-day period changes or rolls forward by one day, each day. It is important to note that this RM could have been interested in calculating her average daily revenue last week (a fi xed average), but she prefers to know her average sales for the last seven days.
This means that she will, at times, use data from both last week and this week to compute the most up-to-date (current) seven-day average. Using the trailing revenue data recorded in Figure 6.6, the seven-day rolling average for this RM would be computed as shown in Figure 6.7.
Note that each seven-day period is made up of a group of daily revenue numbers that change over time. The fi rst seven-day rolling average is computed by summing the fi rst seven days’ revenue (revenue on days 127 5 $9,828) and dividing that number by seven to arrive at a seven-day rolling average of $1,404.00 ($9,828 4 7 5 $1,404.00). Each day, the RM would add the day’s revenue to that of the prior seven-day total and subtract the revenue from the day that is now eight days past. This gives her the effect of continually rolling the most current seven days’ data forward.
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H I STO R I C A L DATA 173
Douglas Lodge
Seven-Day Period
Date 1–7 2–8 3–9 4–10 5–11 6–12 7–13 8–14
1 $ 1,350 —
2 1,320 $ 1,320 —
3 1,390 1,390 $ 1,390 —
4 1,440 1,440 1,440 $ 1,440 —
5 1,420 1,420 1,420 1,420 $ 1,420 —
6 1,458 1,458 1,458 1,458 1,458 $ 1,458 —
7 1,450 1,450 1,450 1,450 1,450 1,450 $ 1,450 —
8 1,460 1,460 1,460 1,460 1,460 1,460 $ 1,460
9 1,410 1,410 1,410 1,410 1,410 1,410
10 1,440 1,440 1,440 1,440 1,440
11 1,470 1,470 1,470 1,470
12 1,460 1,460 1,460
13 1,418 1,418
14 1,494
Total $9,828 $ 9,938 $10,028 $10,078 $10,108 $10,148 $10,108 $10,152 7-Day
Rolling Average
$1,404.00 $1,419.71 $1,432.57 $1,439.71 $1,444.00 $1,449.71 $1,444.00 $1,450.29 Figure 6.7 Seven-Day Rolling Average
Figure 6.6 14-Day Revenue Generation
Douglas Lodge
Date Revenue Date Revenue
1st $1,350 8th $1,460
2nd 1,320 9th 1,410
3rd 1,390 10th 1,440
4th 1,440 11th 1,470
5th 1,420 12th 1,460
6th 1,458 13th 1,418
7th 1,450 14th 1,494
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The use of the rolling average, while more complex and time consuming than that of a fi xed average, can be extremely useful in recording the historical data that will help you make effective predictions about the sales levels, rooms sold or other data you might expect in the future. This is true because in many cases, rolling data are more current and thus more relevant than some fi xed historical averages.
As you design your revenue management program, you may choose to compute fi xed averages for some data and time periods and use rolling averages for others. For example, it may be helpful to know your average rooms sold for the fi rst 14 days of last month and your average rooms sold for the past 14 days. If, for example, the larger number was from last month, you may be experiencing sales decline. If the most recent number is larger, you are likely experiencing an increase in room sales. Either way, the data you have collected and analyzed can help you better predict your future room sales.
Regardless of the type of averages they utilize, RMs must track their historical data because it is from sales histories that they will be better able to predict current and future operating data.
In the previous chapter, you learned that Smith Travel Research (STR) offers RMs a variety of informational reports. STR also reports weekly and monthly demand for rooms (and room rates paid) in a variety of geographic locations both nationally and internationally. You can view the most recent demand reports at:
www.strglobal.com
When you arrive, click News.